Garuda - Garba Rujukan Digital

Design of an Arduino-based automatic sound timer system for mosques and prayer rooms Fadlil, Fadlil; Tri Wahono; Edi Ismanto; Azaki Khoirudin
Journal of Natural Sciences and Mathematics Research Vol. 10 No. 2 (2024): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

The uncontrolled use of sound systems in mosques, especially after the call to prayer and iqomah, often leads to waste of energy and disturbances to the surrounding environment. This research aims to develop an Arduino-based automation system that can set the time of turning on and off the sound system in the mosque according to the adhan schedule, in order to improve operational efficiency and reduce electricity consumption. The system is designed using an Arduino UNO microcontroller, which regulates the sound device via a relay based on manually entered timing. Testing was carried out using Wokwi software simulations, and the results were then applied to a physical prototype. Testing is carried out with various time settings (5, 10, 15, and 20 minutes) to assess the accuracy and reliability of the system. The test results showed that the system was able to control the sound device with a 100% success rate in all the times tested. This system has proven to be effective in reducing the duration of unnecessary use of sound devices, potentially saving energy. The developed system provides a practical solution to reduce energy waste in mosques and improve the operational management of sound devices. Further implementation of this system in mosques can provide significant social and environmental benefits, especially in terms of congregational comfort and energy savings. Field testing as well as the development of IoT-based technologies in the future can expand the functionality of these systems.

Design of an Arduino-based automatic sound timer system for mosques and prayer rooms Fadlil, Fadlil; Tri Wahono; Edi Ismanto; Azaki Khoirudin
Journal of Natural Sciences and Mathematics Research Vol. 10 No. 2 (2024): December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/jnsmr.v10i2.22293

The uncontrolled use of sound systems in mosques, especially after the call to prayer and iqomah, often leads to waste of energy and disturbances to the surrounding environment. This research aims to develop an Arduino-based automation system that can set the time of turning on and off the sound system in the mosque according to the adhan schedule, in order to improve operational efficiency and reduce electricity consumption. The system is designed using an Arduino UNO microcontroller, which regulates the sound device via a relay based on manually entered timing. Testing was carried out using Wokwi software simulations, and the results were then applied to a physical prototype. Testing is carried out with various time settings (5, 10, 15, and 20 minutes) to assess the accuracy and reliability of the system. The test results showed that the system was able to control the sound device with a 100% success rate in all the times tested. This system has proven to be effective in reducing the duration of unnecessary use of sound devices, potentially saving energy. The developed system provides a practical solution to reduce energy waste in mosques and improve the operational management of sound devices. Further implementation of this system in mosques can provide significant social and environmental benefits, especially in terms of congregational comfort and energy savings. Field testing as well as the development of IoT-based technologies in the future can expand the functionality of these systems.

Penguatan Pendidikan Karakter Siswa Melalui Tujuh Kebiasaan Anak Indonesia Hebat di SMK Negeri 3 Pekanbaru Amelia Agustina; Edi Ismanto
Jurnal Pendidikan Dirgantara Vol. 2 No. 1 (2025): Februari : Jurnal Pendidikan Dirgantara
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupendir.v2i1.196

Character education is one of the important aspects in forming a good personality in each individual. Good character will have a positive impact on the social, emotional, and academic life of students. The 7 Habits of Great Indonesian Children Movement Program is designed as a strategic step to form individuals who are not only academically intelligent, but also have strong characters that are the foundation of the nation's success in the future. The main objective of this movement is to create a golden generation of Indonesia in 2045. By instilling positive habits from an early age, it is hoped that Indonesian children can grow into healthy, intelligent, characterful individuals who contribute positively to the nation and state. One of the State Vocational Schools in Pekanbaru has implemented the 7 habits of great Indonesian children movement, namely at State Vocational School 3 Pekanbaru.

Perbandingan Algoritma Random Forest Dan Xgboost Untuk Klasifikasi Penyakit Jantung Berdasarkan Data Medis Pramudya, Muhammad Rayenra Azthi; Celvin Arafat; Muhammad Cavin Ramadhan; Fikri Abdul Jafar; Edi Ismanto
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9927

Penyakit jantung merupakan salah satu penyebab kematian terbanyak di dunia, sehingga deteksi dini menjadi penting untuk mengurangi risiko fatal. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma pembelajaran mesin, yaitu Random Forest dan XGBoost, dalam mengklasifikasikan penyakit jantung berdasarkan data medis. Dataset yang digunakan tersedia untuk umum dan mencakup fitur-fitur darah seperti usia, tekanan, kadar kolesterol, denyut jantung maksimum, hasil EKG, dan tanda-tanda talasemia. Proses penelitian melibatkan eksplorasi data (EDA), pembersihan, transformasi, dan pelatihan model menggunakan kedua algoritma tersebut. Evaluasi dilakukan dengan menggunakan metrik seperti akurasi, presisi, recall, skor F1, dan ROC AUC. Hasilnya menunjukkan bahwa Random Forest berkinerja lebih baik dalam hal sensitivitas dan akurasi dibandingkan dengan XGBoost, terutama dalam mengidentifikasi pasien yang benar-benar menderita penyakit jantung. Temuan ini menunjukkan bahwa metode ensemble berdasarkan keputusan pohon, Random Forest, dapat menjadi pendekatan yang efektif untuk sistem prediksi penyakit jantung dini berdasarkan data medis.

Perbandingan Model Machine Learning Untuk Klasifikasi Deteksi Penyakit Jantung Fatihul Ihsan, Tengku Fawwaz; Ilham Ramadhan; Davie Rizky Akbar; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9811

Heart disease is one of the leading causes of death in the world, so early detection is an important aspect in prevention efforts. This study aims to build a heart disease risk prediction model based on patient clinical data using the Random Forest algorithm. The dataset used consists of 303 data with 13 features such as blood pressure, cholesterol, maximum heart rate, and others, as well as one nested target attribute. The data processing process includes cleaning invalid values such as question marks ('?') which are changed to missing values, and deleting incomplete data to maintain the integrity of the dataset. After going through data exploration and correlation analysis between features, the model is trained using the Random Forest algorithm because of its ability in multiclass classification and resistance to overfitting. The initial evaluation results show that the model has good prediction accuracy with a score reaching 0.89. This study proves that the Random Forest-based machine learning approach is effective in helping the process of systematically identifying heart disease risks, so it has the potential to be a decision support tool in the field of preventive health.

Pemodelan Prediktif Diabetes Menggunakan Pendekatan Multimodel Machine Learning dan Deep Learning Fadli Rahmad Hidayatullah; Afandi Alsyar; Riski Amin Putra; Winson Ardhika Ramadhani; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9812

This study discusses the implementation and evaluation of various machine learning algorithms along with one deep learning model for predicting diabetes based on patient medical data. The dataset underwent Preprocessing steps including categorical feature Encoding, feature scaling, and train-test split. The algorithms compared in this study include Logistic regression, Decision Tree, Random Forest, and K-Nearest Neighbors (KNN). Additionally, a Multilayer Perceptron (MLP) model was developed using Keras to explore a deep learning approach with the use of epochs and batch size. The model performance was evaluated using accuracy, precision, and recall metrics, along with learning curve visualizations to analyze model convergence during training. The evaluation results showed that the Random Forest model achieved the highest accuracy among traditional algorithms, while the MLP provided competitive results with strengths in generalization. Visualization of loss and accuracy per epoch offered deeper insight into model behavior throughout the training process. This study demonstrates that a combination of proper data Preprocessing techniques and appropriate model selection significantly influences prediction accuracy. The findings may serve as an early reference for the development of data-driven medical prediction systems and support computer-assisted clinical decision-making (clinical decision support systems).

Analisis Kinerja Algoritma K-Nearest Neighbors (KNN) dan Random Forest untuk Klasifikasi Kondisi Cuaca Asha Yuda, Agim Sahrija; Muhammad Desfriyan Arif Rosady; Nabil Ibrahim Faisal; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9827

The development of information technology has encouraged the use of machine learning algorithms in various fields, including in the analysis and prediction of weather conditions. This study aims to analyze and compare the performance of two machine learning algorithms, namely K-Nearest Neighbors (KNN) and Random Forest, in the classification of weather conditions based on historical meteorological data. The dataset used includes features such as rainfall, maximum temperature, minimum temperature, and wind speed, with target categories in the form of weather types such as rain, sunny, fog, drizzle, and snow. The process includes data pre-processing, feature scaling, training and test data sharing, and model training using the scikit-learn library. Performance evaluations are conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest model had higher accuracy (82%) than KNN (78%), with more stable performance in the majority class. However, both models experienced significant performance declines in minority classes due to data imbalances. The study recommends further optimizations such as class balancing and model parameter selection to improve the overall accuracy of weather classification.

Analisis kekuatan bearing type NU 314, Bearing QJ 314, dan bearing 22316 pada mesin Screw Conveyor Edi Ismanto; Jatira; Yadi Heryadi
Jurnal Teknologika Vol 14 No 1 (2024): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v14i1.365

Dalam proses produksi salah satu kegiatan yang umum adalah material handling yaitu penanganan atau pemindahan bahan baku dari satu proses ke proses yang lainya. Proses pemindahan material atau material handling dibutuhkan pesawat angkat dan angkut. Salah satu pesawat angkut yang digunakan adalah mesin Screw conveyor. Mesin digunakan untuk memindahkan material berupa clay yaitu berupa butiran butiran tanah yang sudah dikondisikan dengan ukuran tertentu yaitu berukuran 5mm sampai 30mm. Bagian pada mesin ini yang sering mengalami kerusakan adalah bagian bearing, hal ini dikarenakan semua beban untuk mendorong material bertumpu langsung dengan bearing. Untuk menentukan kekuatan bearing yang dipakai maka harus dibuat dulu rancangan dasar dari mesin tersebut, yaitu kapasitas mesin, jenis material, gaya yang bekerja pada bagian bearing. Disini telah didapatkan data kapasitas mesin screw conveyor adalah 11.000 kg/jam, kekuatan bearing secara perhitungan yaitu Bearing QJ304: 15.637 jam, Bearing NU304: 38.683 jam, dan Bearing 22316: 50.937 jam. Untuk mendapatkan kondisi aktual bagian bearing yaitu pengukuran vibrasi. Dalam hal vibrasi alat ukur akan menunjukan besarnya velocity. Velocity adalah jumlah waktu yang dibutuhkan ketika terjadinya displacement atau kecepatan getaran suatu benda. Secara garis besar Velocity mewakili kondisi kerusakan bearing yang diaktualisaikan dengan niali 0-11 yaitu merujuk pada Standard ISO 10816-3. Sehingga akan didapatkan hasil perbandingan dari perhitungan perencanaan mesin dan aktualnya.

Title

Found 8 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 8 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 8 Documents
Search